Digital computers commonly employ image processing techniques such as image digitizing, segmentation, magnitude gradient determination, and topological skeletonization techniques in order to recognize patterns represented in the images. With these techniques, a current digital computer executing suitable software can perform many pattern recognition tasks such as character recognition. However, the more general pattern recognition capabilities such as provided by the human brain can be difficult to replicate on digital computers because of bandwidth and connectivity issues that command astronomical quantities of digital resources. In particular, current digital computer architectures might require 5 to 7 orders of magnitude times the processing power of a human brain to approach the general pattern recognition abilities of the average human. This disparity in abilities is sometimes used to distinguish between human and automated users of websites and other systems. In particular, a web page or other software can request that a user enter words from an image containing text that is distorted. A human can often recognize the pattern of the distorted text even when the distortion is beyond the recognition capabilities of standard character recognition software.
Cellular neural networks (CNN) and neuromorphic networks have been suggested for image processing and pattern recognition systems. These network systems can potentially provide significant improvements in automated pattern recognition, but the systems are generally complex. Further, some proposed neural networks require molecular electronic elements or nanometer scale devices that cannot currently be produced. In view of the limitations of current pattern recognition systems and processes and the complexity of proposed pattern recognition systems, new approaches may be needed.
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